Aiming at the optimization problem of the cloud computing’s service cluster resource schedule and loading balance, this paper presents cloud computing resource schedule strategy based on Modified Particle Swarm Optimization(MPSO) algorithm. In order to control the global search and local search effectively, and to avoid falling into local optimal, it introduces dynamic multi-group collaboration and the reverse of the flight of mutation particles to the Particle Swarm Optimization(PSO) algorithm. By extending the cloud computing emulator CloudSim platform to test the simulation, the results show that this method is effective, and the operation efficiency is high.

It exists problems of big size, high power and low efficiency when cloud computing center is composed of traditional sever. This paper designs and implements a new cloud computing server. It discusses the key technologies, such as cloud processors, 3D-torus net, hardware virtualization, etc. Compared with traditional servers, the new one has the characteristics of small size and low power.

This paper introduces the resource allocation model which is based on the Continuous Double Auction(CDA) mechanism and the pricing strategy of the supplier and the demander under cloud computing environment. After the law of the failure of resources nodes is analyzed, it promotes a dynamic resource allocation strategy which is based on the CDA mechanism and the credibility of the nodes. Matlab simulation results show that the strategy can ensure the creditability of the resources, improve the operation efficiency and maximize the Quality of Service(QoS) when dynamically providing resources.

To ensure the user data security under cloud computing environment, this paper uses homomorphism encryption algorithm for data and encryption function of privacy protection function, and designs a new fully homomorphic encryption algorithm based on integral polynomial ring. Both the homomorphic encryption and the re-encryption are included in the algorithm. The homomorphic encryption is used to encrypt the data, and re-encryption is used to encrypt the encrypted data. Analysis result shows that the proposed algorithm computing complexity O(n5) is lower than ideal lattice fully homomorphic encryption.

Now existing access control scheme has the strong dependence to trusty third party in cloud. With regard to this problem, this paper proposes an access control scheme based on attribute. It designs an access control model in cloud, constructs an access control policy which adopts the way of Attribute-based Encryption(ABE) tree, and presents a method of user revocation and policy updates. Security analysis indicates that the scheme has the features of collusion-resistance, data confidentiality and backward and forward secrecy.

The mass data from Web are distributed, heterogeneous and dynamic, so the current data mining system based on single node has developed to a bottleneck. Using the advantage of cloud computing——distributed processing and virtualization, this paper presents a Weblog analysis platform under the Hadoop’s cluster framework based on cloud computing, it also presents a hybrid algorithm which can distributed process in the cloud computing environment. To further verify the effectiveness and efficiency of the platform, it uses the improved algorithm to mine users’ preferred access path in Weblog on the platform. Experimental results show that, using distributed algorithm to process large number of Weblog files in the cluster, can significantly improve the efficiency of Web data mining.

In order to meet the secure requirements of data sharing in mobile cloud storage environment,a verifiable access control scheme based on Ciphertext-policy Attribute-based Encryption(CP-ABE) is proposed from the aspects of data security access control and integrity verification,to deal with the situations such as inadequate battery power,limited data storage and computation capacity for mobile devices.It introduces the Encryption Service Provider(ESP) and Decryption Service Provider(DSP) into the system model,and implements the security outsourcing of encryption computation by using the permission attribute.ESP generates the verifiable tag for the ciphertext,before decrypting,the data integrity verification is requested by the challenger,and completed by Cloud Service Provider(CSP) according to the verifiable tag.DSP decrypts the ciphertext for the user who requests to access data.Due to the user secret key holding only by the corresponding user,the decryption computation is outsourced in security.The results of analysis and evaluation show that the proposed scheme can reduce the computation overhead of the mobile user by outsourcing computation to other servers.

The service of cloud computing faces the huge user group, with the expansion of node scale and the growth of task execution time, the failure rate of cloud computing is increased. To solve this problem, the fault-tolerant scheduling algorithm for cloud computing based on task backup is proposed. It maps task to the node which contains the input data of the task and the load is the smallest. According to the level of cloud computing security, it backups the task, and re-schedules for the failure task. Simulation experimental results show that the algorithm has good fault tolerance, and the success rate of task scheduling is 99%.

To solve the problem of excessive virtual machine migration and not extend to the large-scale cluster by the centralized management method of virtual machine in cloud data center, this paper proposes a distributed management method of virtual machine for energy-saving. The distributed management method includes the local management and the global management. The local management is responsible for monitoring the hosts of data center and deciding whether to migrate a virtual machine by the high threshold and the low threshold. The global management is responsible for migrating the virtual machine to the physical node which consumes the least power after the migration. Experimental results show that the distributed management method can reduce the number of virtual machine migration and improve the quality of service while achieving better energy efficiency in cloud data center.

There exist the problems of difficulties in evidence obtaining and numerous logs to be dealt with in computer forensics. Aiming at these problems, this paper introduces the cloud computing into computer forensics, proposes a Cloud Forensics Model(CFM). CFM obtains evidence by making use of Agent technology, which can increase autonomy and intelligence of evidence acquisition. It improves the forensics efficiency and the safety of computer evidence by using the virtualization technology and collaboration technology. It introduces the feedback technology, which can consummate the evidence collection system. Experimental results prove the validity of the model.

In order to solve the problem that the conventional approaches for network experiment platforms, which construct an experiment platform either as a physical network directly or in the form of overlay networks, cannot guarantee a high resource utilization and a well network link quality at the same time, this paper proposes a scheme for network experiment platform based on cloud computing and virtualization technology, and provides its preliminary implementation called TUNIE. Result shows that the platform is both flexible in function support and convenience for use, as well as providing a high bandwidth connection and a well-isolated network environment for multi-user support.

With ASIC system’s efficiency and software programmable feature, reconfigurable concept brings further improvement for computer’s performance. In the context of cloud computing applications, reconfigurable systems require a file system to efficient process massive small files on the Internet. Therefore, this paper describes and analyzes existed small file systems, and proposes a Field Programmable Gata Array(FPGA)-based small file system: FPGASmallFS. By simplifying its structure and adjustable disk volumes block, and with FPGA’s acceleration, this file system shows a way to improve the speed of file system and disk space utilization. In the meanwhile with the parallel acceleration of FPGA, it speeds up the file system mount process, so as to improve the system of reconfigurable process. The IOPS performance tests result of the file system proves the better availability of FPGASmallFS system compared with ReiserFS and Ext2 system.

The Quality of Experience(QoE) of mobile terminals severely reduces caused by both local service station to a serious shortage of resources,and mobile terminal resources with limited.Furthermore,the task offloading into remote cloud brings the large time delay.This paper proposes a joint optimization of the power and load allocation algorithm for small cell cloud.Based on the channel quality and the rest of the available computing resources to build Small Cell Cloud(SCC),according to the same element,it distributes the load(offloading tasks) to SCC and uses a heuristic algorithm to seek approximate suboptimal solution to transmitting power.Simulation results show that the algorithm can improve the utilization of the radio and computing resources,and enhance the user QoE at the same time.

Efficient resource provision and schedule is the key to build a private cloud environment. This paper presents a resource provision and schedule approach based on the proposed cloud application meta-model for cloud applications in a private cloud environment. It creates a minimum spanning tree according to the characteristics among virtual machines, and splits the set of virtual machines and maps them to the physical hosts by using the graph theory based on split clustering algorithm. The experiments and comparative analysis of the method for virtual machines show that the approach is efficient in enhancing the overall performance of cloud applications while reducing the consumption of physical network equipments and bandwidth resources.

This paper proposes a cryptographic access control scheme based on attribute group in cloud storage. The symmetric cryptosystem is used to encrypt the original data by the Data Owner(DO), and the attribute-based encryption algorithm is used to encrypt the symmetric key. DO delegate the task of data re-encryption to the Cloud Service Provider(CSP), which not only reduces the computational cost of DO, but also does not reveal extra information of the plaintext to CSP. The proposed scheme supports the unit of a collection of multiple users when any attribute or user is revoked. The fine-grained and flexible access control can be achieved by hybrid cryptosystem meachanism. Compared with the existing access control schemes, the proposed scheme is more efficient in revocation cost and can alleviate the administering burders on DO. The scheme guarantees collusion resistance against colluding users, data confidentiality, forward and backward secrecy.

This paper designs a management system for ICCS-PW cloud server, which can manage power, storage, switch, and system monitoring in one platform by using a combination of hardware and software methods, AJAX, Common Gateway Interface(CGI), custom communication protocols and other technologies. The implementation of this system is divided into three levels: management software, I2C bus and controller chip. The system is tested with LoadRunner, and the results show that the management system can meet the requirements of the application.

To the problem that traditional Genetic Algorithm(GA) has early convergence, this paper proposes an improved Cellular automata Genetic Algorithm(CGA), and applies on resource schedule under cloud environment. It makes a discussion on the enormous tasks required by users group and how to allocate resources reasonably, takes use of CGA to search for the optimal resource schedule proposal. Experimental results under the simulator platform CloudSim shows that the algorithm can reduce the whole makespan and decrease the costs of users, which is an effective resource schedule satisfying cloud environment.

Resource monitoring is an important part of resource management in the cloud computing environment, so as to provide reference for resource allocation, task scheduling and load balancing. The resources in the cloud computing environment are highly virtualization, scalability and transparency, in addition cloud computing target is commercial applications need for statistical resources using of users for billing, so the existed resource monitoring methods can not fully meet the cloud computing environment. According to the characteristics of cloud computing platforms, it presents a novel resource monitoring model while adapt to cloud computing environment, the model by Virtual Machine Monitor(VMM) and the Java call C/C++ to get information of the resource status. Through theoretical analysis and experiments show the model can be used to collect resource monitoring information on nodes, it meets the requirements of cloud computing platform features.

In order to solve the ciphertext data calculation and the privacy protection of private cloud users issues in cloud computing environment, a homomorphic encryption scheme based on the homomorphic characteristics of RSA and Paillier is designed. Considering the fact that lacking an effective fully homomorphic encryption system at present, single homomorphic encryption system is used to construct somewhat homomorphic cloud computing scheme, which can satisfy multiplicative homomorphism and additive homomorphism and solve the ciphertext processing problem of the public cloud server successfully. It can achieve homomorphic calculation in the process of the ciphertext scale control, which has a large depth of the homomorphic calculation and completes the calculation for the data of floating-point types by an appropriate mapping. Simulation results show that compared to directly computing the plaintext, the proposed scheme needs more computing time, but it has higher correctness and security, and can meet various forms of computation requests in cloud computing environment.

Data security is a big block for the promotion of cloud computing, which is mainly derived from data sharing and privileges of vendors. This paper analyzes features of data storage and user groups in cloud computing, proposes a kind of cloud computing access control based on Task-role-based Access Control(T-RBAC) model, which provides different access control policy for different object to achieve hierarchical safety. And vendors enjoy no privileges in this mode. Analysis indicates that this cloud computing access control model provides more reliable security feature which can no longer rely on credibility of server.

Map-Reduce model does not consider the computing performance in large number of heterogeneous nodes when allocating the input files in the cluster, and this leads to network data transfer increase to run the map task. Addressing this issue, this paper proposes the improved Map-Reduce model under the cloud computing environment. For a large number of nodes in the cluster calculation with different performance, it raises the maximum computational time minimal objective function model, uses Genetic Algorithm(GA) to solve the model to get the distribution plan. Simulation results prove the validity of the model.

This paper analyzes the prevalent problems such as massiveness, high-dimension and sparse of feature vector of the ordinary algori- thms in clustering textual data, then proposes a massive text clustering based on cloud computing technology as a feasible solution. The classical Jarvis-Patrick(JP) algorithm is chosen as a case. It is implemented using MapReduce programming mode and is testified on the cloud computing platform-Hadoop with Sogou corpus provided by Sogou laboratory. Experimental results indicate that the JP algorithm can be paralleled in MapReduce framework and paralled algorithm can handle massive textual data and get a better time performance than single-node environment.

On the basis of Google open cloud environment, through the analysis of network topology and location information based on the relationship between the server location in the cloud environment, this paper gets hierarchical network topology map of collection of data blocks in the cloud environment, and gets risk assessment model through the analysis of data block distribution to the storage resources in the cloud environment and gives specific examples of risk calculation algorithms and specific example analysis and provides a theoretical risk basis for cloud storage resources in the cloud environment.

Users usually can only provide incomplete preference information to service providers. In order to carry out effective cloud services composition, this paper proposes an incomplete user preference information weight fixing algorithm. It gets the incomplete information, obtains the user subjective and objective weight by rough set method and then gets the user preference service composition. The algorithm is used in cloud computing platform, and achieves good results.

This paper proposes a High Performance Computing Application Packaging method based on Template(HPC-APT), which is cross-platform, simple syntax, easy maintenance and extension, and user-friendly. HPC-APT supports Web and windows application in main grid middleware or cloud computing platform. HPC users can submit jobs to remote HPC clusters via Web or windows application interface, so that the entry barrier of HPC cluster is greatly reduced and the usability is highly increased.

With the demands for software test cases in exponential growth, the problems such as relative shortage of test resources, high cost of testing and low efficiency execute of test cases are increasingly outstanding. To solve the above problems, a parallel test schema based on cloud computing is proposed, which uses Finite State Machine(FSM) to define test object and state transition in testing process. Parallel test case generation algorithm based on random routes idea is proposed, and a parallel test script based on the MapReduce and cloud computing platform is designed. Experimental results show that compared with test sequence of executing sequential, the acceleration of schema is up to 20, so the test efficiency is significantly improved.

Hosts are usually selected only based on hosts’ current CPU conditions using traditional method. It brings load imbalance caused by the irrational use of resources. Aiming at this problem, this paper presents a virtual machine deployment mechanism in cloud platform. The model which can automatically select host is established. The automatic selection strategy is based on the individual needs, and it can predict follow-up load and resource consumption of virtual machines. Experimental results show that the mechanism can meet the users’ requirement and improve the system performance.

In cloud computing environment,technologies such as dynamic migration and scaling decrease both violations of Service Level Agreement(SLA) and energy cost.But frequent migration of Virtual Machine(VM) can increase SLA violations and cause the performance degradation.To minimize SLA violations and migrations,this paper proposes a scalable real-time VM scheduling policy.It builds a scalable system with Kafka and Spark to analyze history data,predicates future load and generates migration plan.Simulation experimental result shows that the policy decreases migrations by 50% while maintaining low SLA violation rate compared with native policy of CloudSim.

With the rise of Cloud Manufacturing(CMfg) and trend in development of large-scale enterprise on manufacturing resulted in new demands for business collaboration, the new demands of collaborative workflow management for enterprise group in cloud manufacturing are analyzed, and four features of such new management are indicated, autonomy, simultaneous, collaboration, and dynamic. A control model of collaborative workflow based on Hierarchical Finite State Machine(HFSM) is presented, the simplified class structure of the model is designed, the dynamic reconfiguration and behavior control mechanism of the HFSM are described. The proposed model is applied to marketing business collaboration of an iron and steel corporation, results show the validity of the model can increase efficiency of business collaboration and improve marketing business ability obviously.

Considering the cost and flexibility,building a temporary private cloud in public cloud to replace the local physical cluster is an effective way for users to run a large number of parallel jobs during a particular period. However,the existing scheduling algorithms are not suitable for temporary private cloud hosting tightly coupled parallel applications, because they can not use the logic topology information of temporary private cloud,which probably results in the performance degradation of such type of applications. To mitigate such impact, a logic-topology-aware scheduling algorithm is presented. It builds the logic topology of virtual machines according to bandwidth and latency among them, makes scheduling decisions based on the logic topology and the information of applications. Experimental results show that compared with Round Robin and Random algorithms of open-source cloud software,logic-topology-aware scheduling algorithm improves the performance of tightly coupled parallel applications.

Aiming at the service deployment problem of geographically distributed clouds,a Short-term Prediction based Elastic Service Scaling(SPESS) is proposed.The algorithm predicts the arrival rate of user requests by Dynamic Autoregressive Integrated Moving Average(D-ARIMA) model.In comprehensive consideration of the prediction results,current load and processing rate of the service,the number of virtual machines in every site is decided to strike a balance between quality of service and cost.Experimental results show that the algorithm can ensure better balance between quality of service and cost,and the load of service,transmission delay and queueing time are controlled in a reasonable range.

For the instability of security and availability of resource in the heterogeneous and complex cloud environment,a DAG task scheduling strategy is proposed to satisfy the demand for security and availability of cloud tasks,which is integrated with both security and availability in cloud computing environment.The strategy first modifies the subjective logical trust model to calculate the reputation of cloud resource that is the basis of scheduling,and then qualifies the availability deviation of tree cloud resource and DAG task,and finally defines the schedule function and greedy scheduling algorithm integrating with security and availability,called ISAG.Simulation results prove that ISAG can efficiently meet the requirement of tasks on cloud platform of low availability and low security,and gets better performance and higher execution success rate compared with DLS and Afsa.

In order to solve the problem which current monitoring system has in reflecting the mass nodes and high coupling of cloud computing cluster, this paper designs a cloud computing monitoring system based on thermodynamic space theory. By projecting node status parameters onto phase space, it converts node parameters change into the movements of projective points in phase space. Consequently, macro monitoring of cloud computing cluster is realized by utilization of phase space images and parameters. Tests results show that the monitoring system can real-time and effectively reflect the overall loading and working status of cloud computing cluster, accomplish monitoring the cluster from a macro level.

The cloud computing service level agreement often only focuses on performance while seldom emphasizes security parameters. Besides, customers are confused by several suppliers in cloud computing market. Therefore, cloud computing Security Service Level Agreement(SecSLA) with detailed indexes is put forward and an improved ELECTRE method is introduced to the cloud provider selection process. Goal-Question-Metric method is employed to form 50 bottom evaluation indexes for SecSLA. The modified ELECTRE method combines the value of net advantage and net disadvantage to simplify the decision process.The calculation and analysis of instance prove the following aspects: the proposed SecSLA has a relatively comprehensive coverage of the cloud computing assessment factors, such as incident handling, vulnerability and patch management,compliance and availability; the suggested method eliminates the redundancy and dependence on experience of ELECTRE II, which makes the realization of automatic process easier. The final evaluation order conforms to the result of ELECTRE II, gray correlation method and TOPSIS method, which indicates that the designed method can play a guiding role in the selection of cloud service provider.

Aiming at the problem of NP hard optimization in the process of cloud computing Virtual Machine(VM) resource allocation,a new algorithm based on cloud computing Simulated Evolution-First Fit Decreasing(SME-FFD) is proposed.The optimal degree evaluation scheme of virtual machine resource allocation is put forward by use of the strong ability of climbing of simulated evolution,and for which the choice of virtual resource allocation,evaluation and sorting process is carried out.The FFD rule is adopted to the sort of virtual machine and physical host resource allocation to improve the efficiency and effectiveness of resource allocation.By comparing the experimental results with the CloundSim grid laboratory and Gridbus cloud simulation platform,it shows that the proposed algorithm is more than 55% of CPU usage,memory usage rate can reach more than 60%,which can improve the utilization rate of the host resources,and achieve the purpose of energy saving.

Big data applications meet various challenges in data migration in cloud computing environment.It mainly manifests in below aspects:reduce the number of network access,reduce the overall time consumption and improve the efficiency by the time of balancing the global load in the migration process and so on.Facing these challenges,it builds the problem model and descripts the dynamic migration strategy,then solves the global time consumption of data migration,the number of network access and global load balance in these three parameters.The cloud computing simulation experiment is done under Cloudsim experimental platform.The result shows that the proposed data dynamic migration strategy makes the task completion time reduced by 10% than Zipf distribution,network access number be lower than Zipf and tends to be stable.And in global load,the variance of the node’s store space is closed to zero.

With the power grid enterprises network education training system concentrated deployment, the system faces a business peak traffic. To solve this problem, this paper presents a design idea of construction of dynamically extensible application system based on cloud computing, puts forward the system architecture. It includes dynamic cluster extension, system integration based on sharing message bus of publish/subscribe model and data decoupling. Through constructing experimental environment, it simulates the scene that through dynamic allocate resources to support the big concurrent access pressure. The simulation results show that the network education training system based on cloud computing can meet the demand of resource for big concurrent pressure, and ensure the user experience.

This paper proposes a two-level scheduling strategy to address the performance differences,various combinations of cipher service processing commands and cipher algorithms,and random high concurrency of computing nodes in a heterogeneous cloud environment.The strategy supports various cipher processing commands and algorithms.Considering the multiple attributes of request tasks of users and computing nodes in the cloud,it optimizes the Quality of Service(QoS) and success rate of task scheduling of the whole scheduling system from both the task viewpoint and the node viewpoint.Through the mapping of function attributes between tasks and computing nodes,the cipher service requests can be realized correctly.On this basis,the node priority algorithm is used to improve the real-time performance of task processing and success rate of task scheduling in a cipher service system with random high concurrency.Simulation results show that the proposed strategy can guarantee the success rate of task scheduling and effectively improve the execution efficiency and task load balancing performance.Compared with the Dynamic Priority Assignment(DPA) scheme and Genetic algorithm(GA),it can shorten task execution by around 4% and 17% respectively.

Since the nodes of the volunteer cloud have the characteristics of high dynamic and low reliability,the volunteer cloud is prone to Byzantine fault.Byzantine consensus algorithm can make the system keep consistent when f malicious nodes are present.However,the existing algorithms have high redundancy degree.Aiming at this problem,this paper proposes a Byzantine fault tolerance algorithm based on Gossip protocol,which reduces the system redundancy degree to 2f+1.It does not need to set the master node and all the computing nodes in system are set to be in peer status,so as to avoid single point failure in master-slave redundancy system.Theoretical analysis and experimental results show that the proposed algorithm can not only satisfy the Byzantine tolerance requirement,but also reduce the system redundancy degree.Compared with BFTCloud and Zyzzyva algorithm,it improves the system throughput.

The resource scheduling policies for the cloud platform are too simple to meet the needs of the medical service effectively.Aiming at this problem,this paper analyzes different demands of different medical systems on resources and proposes the IB-Choose resource scheduling strategy on this basis.It builds a medical cloud platform with doctor diagnosis and treatment system,laboratory test system and image archiving system based on OpenStack platform,and implements the IB-Choose strategy on this platform.Experimental results show that,compared with the default resource scheduling policy in OpenStack,named Chance,the IB-Choose resource scheduling policy can shorten the service time of starting virtual machines by 25%~30%.At the same time,it reduces the cloud resources overhead and improves the utilization rate of cloud resources.

Aiming at the problem that the virtual machines of OpenStack cloud platforms fail to better utilize host physical machine resources,this paper builds a OpenStack platform based on Docker container technology which combines the efficiency of OpenStack based on component work in cloud platform management and the rapid deployment of Docker container virtualization.It tests the basic performance of the guest OSs of Docker container in the cloud platform,including computing,scheduling,memory access and I/O performance,evaluates and analyzes the related performance and compares its performance with the traditional OpenStack virtual machine in a multi instance running state.Analysis result shows that the new cloud platform can optimize the computation performance and the file system I/O performance from a global perspective by using the lightweight virtualization capabilities of Docker container.

To solve the problem of resource scheduling problem in cloud computing,a parallel scheduling model is proposed,which can improve the task parallelism while maintaining the serial relationships between tasks. Dynamic tasks submitted by users are divided into sub-tasks in some serial sequences,and it puts into scheduling queue with different priorities according to running order. For these tasks in the same priority scheduling queue,an improved Delay Time Shortest and Fairness Ant Colony Optimization(DSFACO) algorithm is applied to schedule. Considering both fairness and efficiency,DSFACO algorithm applies to subtask scheduling problem to realize shortest delay time,thus improves the user satisfaction. Experimental results show DSFACO algorithm is better than the TS-EACO algorithm in fairness, efficiency and task delay time,and it can realize the optimal scheduling in cloud computing.

In order to solve the problem of load imbalance of Distributed Hash Table(DHT) cloud storage system, this paper proposes a load balancing algorithm based on Node Dynamic Forward(NDF). Through overload nodes forward, this algorithm can reduce the node’s partition, and thereby reduce the storage node load. At the same time, copy the corresponding data to the third successor node, to ensure the stable number of copies the data in the system. In the NDF implementation process, it only needs a simple coordination between overload node and the third successor node. Therefore, multiple overloaded nodes can concurrently do load transfer, which is suitable for deployment in a large cluster. The functional test in a minor cluster of 10 nodes verifies that the NDF has good ability of load balancing. The performance test in a massive cluster of 5 000 nodes demonstrates the total system load does not exceed 60%, and compared with the virtual node algorithm, NDF algorithm can improve the load transfer cost by more than 30%.

The existing Virtual Machine(VM) placement researches mostly aim at energy saving,which reduce energy consumption by consolidating of resources.However,the excessive consolidation of these resources may lead to network performance degradation.Aiming at this problem,this paper studies the network-aware VM placement problem,analyzes the influence factors of VM placement and proposes a two-phase heuristic VM placement algorithm.Firstly,based on analyzing the similarity between VMs,the aggregation is performed to improve communication ability among VMs and reduce the network traffic of data center.Then,a modified knapsack algorithm is used to implement the appropriate allocation of VMs between physical hosts.Experimental results show that,compared with Best Fit(BF) algorithm and random algorithm,the proposed algorithm can optimize the network traffic,reduce the number of avtivated physical hosts and save energy consumption more effectively.

When Infrastructure as a Service(IaaS) cloud computing center’s resources need to be reallocated for some reason, Minimization of Migrations(MM) strategy has more migrations when choosing migration set in processing live migration. It increases the violation probability of Service Level Agreement(SLA) and generates a lot of useless energy consumption. Considering this problem, it proposes a modified strategy Find-Migration-Search(FMS). Through the history data of services, it can get the status of each virtual machine. Then the program scans each physical machine with two phase. CloudSim simulation results indicate that the modified strategy FMS can not only lower the times of virtual machines’ migration and energy consumption, but also satisfy services’ SLA much better, and implements resources optimization deployment under IaaS model.

Marine monitoring data has the characteristics of large scale and strong correlation.How to effectively layout the data and how to improve the execution efficiency of data management and application are the keys in current marine data research.With the integration of Internet-plus and digital ocean,a marine monitoring data layout strategy in cloud environment based on the correlation of monitoring data is proposed.In view of the characteristics of marine monitoring data in digital ocean,a strong correlation matrix is established according to the correlation of monitoring tasks,monitoring points and monitoring data.This brings data with high correlation together in the matrix arrangement.It divides the data based on the correlation matrix.Consequently,the data in different group can be distributed to different data center based on the capacity.Experimental results show that the strategy reduces the running time of the algorithm and the response time of marine monitoring data access.Besides,the strategy provides an effective method to the management and layout of marine monitoring data in digital ocean.

In order to improve the utilization rate of cloud resource scheduling and keep load balance,chaos firefly algorithm is proposed for resource scheduling in cloud computing. Taking into account task completion time, task completion efficiency and task completion safety,a cloud resource allocation model is established. Through introducing chaos algorithm into firefly algorithm, disturbing individuals and strengthening rate of convergence, it lowers the probability of local optimum. Lagrange relaxation function is introduced for lack of resource scheduling in cloud computing. Simulation experimental result shows that the improved algorithm can effectively avoid imbalance in resource allocation,shorten completion time of task and enhance integrated processing capacity of system.

In order to accurately and efficiently verify data integrity in smart grid with cloud storage,this paper proposes a new scheme for dynamic data integrity verification.On the basis of protecting the data confidentiality,this scheme performs BLS short signature on data and supports third party auditing,which can verify the integrity of partial data and reduce the computational cost according to user requirements.Meanwhile,it uses the Locality Sensitive Hash(LSH) as an quick searching manner to improve the query efficiency of updating the stored data and quickly retrieve the data while integrity being validated.Experimental results show that the proposed scheme can verify the integrity of the power data accurately and support efficient dynamic data updating.

Complex Event Processing(CEP) is an effective approach for real-time event stream monitoring,processing,analyzing and mining.Core concepts and basic elements of CEP are introduced.Major areas that utilizing CEP technology are reviewed,such as Internet of Things(IoT),cloud computing,grid computing,Business Process Management(BPM) and computation finance.Based on these applications,cloud based distributed parallel CEP technologies are introduced.By analyzing the requirements of future stream processing systems,it is concluded that such cloud based CEP technology is an important direction for the development of future CEP systems.Potential new areas that require such cloud based CEP system for complex stream event processing are explored,such as power system intelligent control,instrument control and intelligent measurement.Guidance can be provided for the utilization of the CEP technology for now and future.

Current remote attestation schemes for cloud computing lack researches on trusted node of Infrastructure as a Service(IaaS). They can not effectively prevent the attestation from untrusted nodes based on IaaS. To overcome these shortcomings, this paper puts forward a threshold remote attestation scheme based on Trusted Cloud Computing Platform(TCCP), which uses verifiable secret sharing algorithm and threshold signature algorithm to implement the remote attestation of trusted nodes in the cloud environment. It also can evaluate the running state of the node and the cluster to implement dynamic metrics for each node in the cluster. The scheme can prevent conspiracy attack and the remote attestation of untrusted nodes based on the assumption of the Collusion Attack Algorithm with K traitors (K-CAA) in the random oracle model. Different critical trusted threshold values are tested in the research. The results show that efficiency of the scheme increases with the threshold value increases and critical trusted threshold decreases.